Evaluating Twitter Influence Ranking with System Theory

Drakopoulos, Georgios; Kanavos, Andreas; Tsakalidis, Athanasios K.

doi:10.5220/0005811701130120

Cited by 18 publications

(10 citation statements)

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“…We briefly discuss how this could be done for graphs. Graphs are commonly used to represent various kinds of data [10,21]. Such graph data is generally organized as a set of graph nodes, each of which has edges to other nodes in the graph.…”

Section: Models For Other Data Structures and Analysesmentioning

confidence: 99%

Scalable pointer analysis of data structures using semantic models

Fegade

Wimmer

2020

Proceedings of the 29th International Conference on Compiler Construction

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Pointer analysis is widely used as a base for different kinds of static analyses and compiler optimizations. Designing a scalable pointer analysis with acceptable precision for use in production compilers is still an open question. Modern object oriented languages like Java and Scala promote abstractions and code reuse, both of which make it difficult to achieve precision. Collection data structures are an example of a pervasively used component in such languages. But analyzing collection implementations with full context sensitivity leads to prohibitively long analysis times. We use semantic models to reduce the complex internal implementation of, e.g., a collection to a small and concise model. Analyzing the model with context sensitivity leads to precise results with only a modest increase in analysis time. The models must be written manually, which is feasible because a model method usually consists of only a few statements. Our implementation in GraalVM Native Image shows a rise in useful precision (1.35X rise in the number of checkcast statements that can be elided over the default analysis configuration) with a manageable performance cost (19% rise in analysis time). CCS Concepts • Software and its engineering → Automated static analysis.

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Section: Models For Other Data Structures and Analysesmentioning

confidence: 99%

Scalable pointer analysis of data structures using semantic models

Fegade

Wimmer

2020

Proceedings of the 29th International Conference on Compiler Construction

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“…In the last decade, property graph databases [34] such as Neo4j, JanusGraph and Sparksee have become more widespread in industry and academia. They have been used in multiple domains, such as master data and knowledge management, recommendation engines, fraud detection, IT operations and network management, authorization and access control [52], bioinformatics [39], social networks [17], software system analysis [25], and in investigative journalism [11]. Using graph databases to manage graph-structured data confers many benefits such as explicit support for modeling graph data, native indexing and storage for fast graph traversal operations, built-in support for graph algorithms (e.g., Page Rank, subgraph matching and so on), and the provision of graph languages, allowing users to express complex pattern-matching operations.…”

Section: Introductionmentioning

confidence: 99%

Cypher

Francis

Green²,

Guagliardo

et al. 2018

Proceedings of the 2018 International Conference on Management of Data

333

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The Cypher property graph query language is an evolving language, originally designed and implemented as part of the Neo4j graph database, and it is currently used by several commercial database products and researchers. We describe Cypher 9, which is the first version of the language governed by the openCypher Implementers Group. We first introduce the language by example, and describe its uses in industry. We then provide a formal semantic definition of the core read-query features of Cypher, including its variant of the property graph data model, and its "ASCII Art" graph pattern matching mechanism for expressing subgraphs of interest to an application. We compare the features of Cypher to other property graph query languages, and describe extensions, at an advanced stage of development, which will form part of Cypher 10, turning the language into a compositional language which supports graph projections and multiple named graphs.

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“…where parameters α 0 and γ 0 can be estimated by, for instance, a least squares method [25]. Additionally, the estimated value of α 0 serves as a quality indicator, as it should be as close to [2,3] as possible.…”

Section: Community Coherencementioning

confidence: 99%

“…Both metrics can also be treated deterministically, especially in the context of social network analysis [23,24]. Community structure discovery provides insight to the inner workings of a particular graph [7,8,17], while metrics such as those in [25] control the discovery process quality. Persistent graphs can be instrumental in designing rollback capabilities in graph databases [26].…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Random Walkers with Second Order Bounds: An Asymmetric Analysis

2017

Self Cite

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Edge-fuzzy graphs constitute an essential modeling paradigm across a broad spectrum of domains ranging from artificial intelligence to computational neuroscience and social network analysis. Under this model, fundamental graph properties such as edge length and graph diameter become stochastic and as such they are consequently expressed in probabilistic terms. Thus, algorithms for fuzzy graph analysis must rely on non-deterministic design principles. One such principle is Random Walker, which is based on a virtual entity and selects either edges or, like in this case, vertices of a fuzzy graph to visit. This allows the estimation of global graph properties through a long sequence of local decisions, making it a viable strategy candidate for graph processing software relying on native graph databases such as Neo4j. As a concrete example, Chebyshev Walktrap, a heuristic fuzzy community discovery algorithm relying on second order statistics and on the teleportation of the Random Walker, is proposed and its performance, expressed in terms of community coherence and number of vertex visits, is compared to the previously proposed algorithms of Markov Walktrap, Fuzzy Walktrap, and Fuzzy Newman-Girvan. In order to facilitate this comparison, a metric based on the asymmetric metrics of Tversky index and Kullback-Leibler divergence is used.

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Evaluating Twitter Influence Ranking with System Theory

Cited by 18 publications

References 0 publications

Scalable pointer analysis of data structures using semantic models

Scalable pointer analysis of data structures using semantic models

Cypher

Fuzzy Random Walkers with Second Order Bounds: An Asymmetric Analysis

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